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Publications associées (35)
Functional data analysis with rough sample paths?
Victor Panaretos, Neda Mohammadi Jouzdani
Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...
TAYLOR & FRANCIS LTD2023Normal approximation of the solution to the stochastic heat equation with Levy noise
Given a sequence L & x2d9;epsilon of Levy noises, we derive necessary and sufficient conditions in terms of their variances sigma 2(epsilon) such that the solution to the stochastic heat equation with noise sigma(epsilon)-1L & x2d9;epsilon converges in law ...
SPRINGER2020The n-term Approximation of Periodic Generalized Levy Processes
Michaël Unser, Julien René Pierre Fageot, John Paul Ward
In this paper, we study the compressibility of random processes and fields, called generalized Levy processes, that are solutions of stochastic differential equations driven by d-dimensional periodic Levy white noises. Our results are based on the estimati ...
SPRINGER/PLENUM PUBLISHERS2020Gaussian and sparse processes are limits of generalized Poisson processes
Michaël Unser, Julien René Pierre Fageot, Virginie Sophie Uhlmann
The theory of sparse stochastic processes offers a broad class of statistical models to study signals, far beyond the more classical class of Gaussian processes. In this framework, signals are represented as realizations of random processes that are soluti ...
ACADEMIC PRESS INC ELSEVIER SCIENCE2020Extremal behaviour of aggregated data with an application to downscaling
Sebastian Engelke, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville
The distribution of spatially aggregated data from a stochastic process may exhibit tail behaviour different from that of its marginal distributions. For a large class of aggregating functionals we introduce the -extremal coefficient, which quantifies this ...
OXFORD UNIV PRESS2019